Your IQ can be reliably predicted by simple reaction time tasks – perhaps even more reliably than with much more complex cognitive tasks. This surprising psychometric fact has led to the belief in human “processing speed.” In the same way that a computer with a faster microprocessor might carry out more computations, with potentially less demand on memory, the idea is that brains with better neuronal efficiency also manifest both higher IQ and proportionately faster reaction times even in simple tasks.

To me, this story always seemed “too good to be true” – or perhaps merely “too simple to be true” – and a recent paper confirms this intuition (big hat tip to Kevin McGrew). The new paper, by Helmbold, Troche and Rammsayer, indicates that “temporal acuity” may be the reason that such simple reaction time tasks correspond with higher level cognition: the temporal resolution of neuronal processing, rather than its absolute speed, may be the factor that matters.

Previous work had identified that subjects’ ability in fine-grained temporal tasks (temporal order judgement across just a few milliseconds; auditory flutter fusion [the point at which a pulsed sound is perceived as continuous], and two-tone discrimination over short durations) predicts general intelligence better than the simple reaction time tasks. The authors confirmed this idea by administering many tasks (described individually here) to 260 adults, aged 18 to 39 years, with a broad range of education levels. The tasks fall into 3 broad categories: traditional intelligence assessments, reaction time tasks, and temporal acuity tasks.

The authors analyzed the results by extracting variation in each set of tasks that was consistent within individuals (known as factor analysis) to arrive at three factors: a general intelligence factor, a reaction time factor, and a temporal acuity factor. The authors then attempted to fit various particular theoretical assumptions to the data (known as structural equation modeling).

The results:

The reaction time and temporal acuity factors were significantly better at predicting general intelligence if they were allowed to correlate with one another, suggesting important overlap in these measurements. Further analyses indicated that only the temporal acuity factor had additional, independent predictive value that went beyond this overlapping variance.

The authors suggest that temporal acuity is incorporated in the reaction time measurements, and that fact accounts for their shared relationship with general intelligence. The more important and predictive variable is the cognitive ability measured by temporal acuity tasks, which they take to reflect the rate of neuronal oscillations.

I think this is an interesting (but probably wrong) idea, for the following reasons:

1) As the authors acknowledge, they only used the Hick paradigm to measure information processing speed, meaning that the factor extracted from this task might include a lot of uninteresting task-specific (as opposed to “cognition-specific”) variance.

2) I was under the impression that Raven’s matrices are the gold standard fluid IQ test – but these weren’t included here. So I wonder about the validity of their general intelligence factor.

3) The authors are making claims about neuronal processing based on behavioral data. Although behavioral data isn’t completely opaque with respect to mechanisms, there are many other more obvious explanations which go unmentioned (but which I describe below).

4) The most obvious way of testing the idea that neuronal oscillation rate predicts intelligence is to use data from intracranial or even EEG recordings, and thus to directly quantify the neuronal oscillations which supposedly correlate with general intelligence. Although such data is plentiful, no one (to my knowledge) has ever reported such a correlation.

5) Most of the temporal tasks all involve “oddballs” – stimuli which are contextually infrequent or otherwise “deviant” from the adjacent stimuli. Oddball detection may be an important individual difference in its own right, and I’m not sure temporal acuity would predict general intelligence so strongly if this task characteristic wasn’t so strongly represented in the tasks.

There is probably some truth to the idea that the temporal coherence of neural processing has potentially ubiquitous effects on behavior, in much the same way that general intelligence is the manifestation of a consistent, ubiquitous individual difference variable.

But “temporal acuity” is probably itself highly multifactorial, dependent on the precise regions of the brain most involved in a particular task and how efficiently they can interact to support performance. Still, temporal processing is a very promising – and often ignored – approach for determining how the brain gives rise to general intelligence.

Intelligence Tests: verbal comprehension (detection of spelling mistakes), verbal fluency (recognition of scrambled words), spatial ability (3-d interpretation of 2-d objects), flexibility of closure (“detection of single elements in complex objects”), arithmetic processing (simple and complex math problems), memory (verbal, numerical, and spatial), and various tests of reasoning from Cattell’s culture fair intelligence test (which are described in a way that makes me think they used matrices).

Timing tasks: two tests of duration discrimination (in the first, subjects had to say which of two tones was longer where neither tone was longer than 1.2sec; in the second, subjects had to say which of six tones was most similar in duration to a previously presented tone, where tones ranged from .7 to 1.3 sec or from 42 to 108msec), temporal order judgment (in which a light and tone would both occur, with inter-stimulus separation determined adaptively to ensure every participant got 75% correct), rhythm perception (detection of a variation in rhythmicity, where 4 of 5 click sounds were separated in time by 150ms and the other interval was separated by some longer time, determined adaptively to ensure every participant got 75% correct);

Reaction time task: 1, 2 and 4 choice reaction time Hick paradigm (subjects have to press one of 1, 2, or 4 buttons as fast as possible in response to a visual stimulus, specifying which button should be pressed).

Hi Chris, great blog!
There’s something I want to run by you, not directly related to to “temporal resolution”.
I suspect that for complex tasks there’s a trade-off between reaction speed & analytical depth. Assumming that neocortex is some sort of hierarchy of generalization, from primary to associative areas, the depth of this hierarchy would increase response delay for non-rutine stimulae.
Given fixed resources for conceptual network, there is a trade-off between the number of nodes (neurons, mini-column, column) & the number of connections per node. As all connections are searched in parallel, their number will increase speed of search. Fewer connections per node would mean that to cover the same area the signal will have to travel between nodes sequentially, increasing delay.
On the other hand, if each node performes discrimination/generalization function, more nodes would mean deeper analysis/selectivity.
So, flat hierarchy would be optimized for speed, while the deep one will ultimately discover higher-generality concepts.
I guess this would show as a trade-off between, correspondingly, white & grey matter in the cortex.
Do you know of any evidence, pro or contra?
Thanks!
Boris.

It’s pure speculation to say how this might relate to your topic, but one might think the peculiar trajectory of these changes are an attempt to deal with the depth/processing speed tradeoffs you mention. This is pretty far out though.

All the evidence I know about RT and IQ shows the correlation described above, but that’s mostly with adults. I could imagine that if there were a speed/depth tradeoff it might manifest more in children.

Thanks Chris, but I think the IQ is not very helpful here. Its correlation with RT is somewhat tautological: it measures an ability to discover patterns within time limits of a test. A deep (slow) hierarchy is at disadvantage here, and it may take years to discover correspondingly deep patterns. About the only way to test it is original scientific work, although it might be better correlated with an “executive function”,- prioritising higher-generality patterns vs. lower-generality ones. I also think differences between adults are greater than between children, resulting from specialization on one’s strengths, greater independence and reduced peer pressure.
One way to test my guess is to look at hemispherical assymmetry: it seems that left hemisphere has a higher generality (semantic) bias. You had a post on that, & the evidence is ambiguous. There’s actually more white matter, but the same number of longer-range connections among columns. The authors of the study you mentioned think this results in more “local” processing (as I would predicted), but why? I think there’s new evidence for non-synaptic (diffusion-based) communication among neurons,
which would be weaker with the distance & myelination, even though the number of connections is the same?